Iterative tiered clustering (ITC) guided tutorial
Kyle Kimler and Ben Doran
Compiled: June 15, 2021
ITC iteratively explores axes of variation in scRNAseq data by clustering and subclustering until variation between cells becomes noise. The philosophy of ITC is that every axis of variation could be biologically meaningful, so each should be explored. Once every possibility is explored, curation and a statistical interrogation of the resolution are used to collapse clusters into the elemental transcriptomes of the dataset. ITC inherently builds a tree of subclustering events. As data is separated by the major axes of variation in each subset, further rounds capture less pronounced variables. This comes with some caveats: variation shared by all celltypes make up one of the major axes of variation in the first round of clustering. Celltypes can split up at the beginning, so the same splitting of e.g. B and T cells might happen further down in separate branches. Thus the resulting clustering tree is neither indicitave of true distances between end clusters nor a tree of unique groupings. We address this by calculation of a binary tree of manually curated end clusters, providing a distance between elemental transcriptomes.
ITC is meant to be a modular software. Currently it iterates through the default Seurat analysis pipeline, with two additions that allow automation:
- PCs used in clustering are chosen by 15 percent improvement of variance explained
- clustering parameters are chosen by maximization of silhouette size on a downsampled parameter scan
Normalization, dimensionality reduction, clustering, and plotting methods are set apart in the code in modularized functions to enable customization.
Ben Doran decided to write the software to address the fact that we were constantly manually subclustering scRNAseq data, and because other iterative clustering softwares like scPanoView and iterclust are tailor built for specific clustering methods outside of the Seurat paradigm.
Model datasets:
- pediatric FGID: 89849 cells x 22431 genes
- Nasal Polyp (Ordovas-Montanes et al 2018) scRNAseq: 19196 cells x 22360 genes
FGID ITC
Reproducing figures for the 2021 paper
Running ITC
The github page describes ITC
ITC is called directly on a seurat object:
srobj <- readRDS("/path/to/full_seurat_object.rds")
endclustSrobjs <- GenTieredclusters(srobj,
saveSROBJdir = "~/tieredoutput/srobjs",
figdir = "~/tieredoutput/figdir",
SaveEndNamesDir = "~/tieredoutput/endclusts")Specifying output directories saves QC statistics
clustering decision metrics
differential expression results among clusters (and visualizations) at each level in ‘figdir’
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## 1: 7.468466e-307 2.148874 0.622 0.061 1.695342e-302 0 CD3D
## 2: 1.898258e-290 2.127852 0.664 0.128 4.309047e-286 0 TRAC
## 3: 3.575767e-214 1.774795 0.479 0.045 8.116990e-210 0 CD3E
## 4: 5.584884e-210 1.798812 0.644 0.225 1.267769e-205 0 IL32
## 5: 9.468750e-209 2.275919 0.472 0.055 2.149406e-204 0 KLRB1
## 6: 1.609778e-208 1.882106 0.480 0.044 3.654196e-204 0 CD2
and UMAP visualizations at each tier.
It also saves subset seurat objects for each cluster in ‘srobjs’, and saves files with lists of cells per end-cluster in ‘endclusts’.
Working with ITC results
I prefer to load tiered cluster data into a dataframe, which I can use directly for comparisons or which I can reattach to the full seurat object’s metadata for annotation, dendrogram production, and other analysis.
LoadTiersAsDF <- function(folder='./tieredoutput/endclusts',maxtiers=10) {
tierfiles <- list.files(folder,full.names=TRUE,recursive=TRUE)
sample_strings <- sub('\\..*$','',basename(tierfiles))
sample_strings <- sub('_T0C0_', '',sample_strings)
tiers <- map2(tierfiles, sample_strings, ~fread(.x,header=FALSE) %>% mutate(id = .y))
tiers <- rbindlist(tiers) %>% data.frame
tiers$id <- gsub('_','.',tiers$id)
tiers <- tiers %>% separate(id,into=paste0('tier',1:maxtiers),sep='\\.',remove=FALSE)
tiers <- tiers %>% rename(CellID=V1)
tiers <- tiers %>% data.frame %>% rename(tierNident=id)
return(tiers)
}fgtiers <- LoadTiersAsDF('~/PREDICT_3p_Paper/FGID/tieredoutput/endclusts')print(head(fgtiers))## CellID tierNident tier1 tier2
## 1 p049_T0D_ILE_LPS_3p_AAGACCTTCCCATTTA-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## 2 p049_T0D_ILE_LPS_3p_ACGGGCTTCAAACCAC-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## 3 p049_T0D_ILE_LPS_3p_ACTGAGTAGTTCGATC-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## 4 p049_T0D_ILE_LPS_3p_AGCTTGACAGCCTGTG-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## 5 p049_T0D_ILE_LPS_3p_AGTTGGTTCACTGGGC-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## 6 p049_T0D_ILE_LPS_3p_CATGGCGAGACGCTTT-1 T0C0.T1C4.T2C0.T3C5.T4C3 T0C0 T1C4
## tier3 tier4 tier5 tier6 tier7 tier8 tier9 tier10
## 1 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>
## 2 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>
## 3 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>
## 4 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>
## 5 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>
## 6 T2C0 T3C5 T4C3 <NA> <NA> <NA> <NA> <NA>Add ITC annotations to the seurat object
srobj <- readRDS('~/PREDICT_3p_Paper/FGID/srobj.rds')srobj@meta.data <- srobj@meta.data %>% cbind(as.data.frame(str_split_fixed(srobj@meta.data$CellID,"_",n=6))) %>% dplyr::rename(patient = V1, time_point = V2, tissue = V3, sample_site = V4, seqDirection=V5,barcode=V6)
srobj@meta.data <- srobj@meta.data %>% left_join(fgtiers,by="CellID")
print(head(srobj@meta.data))## CellID curatedname patient
## 1 p009_T0D_ILE_LPS_3p_AAACCTGAGACTGTAA-1 T/NK/ILC.MAF.RPS26 p009
## 2 p009_T0D_ILE_LPS_3p_AAACCTGAGTAGCCGA-1 T/NK/ILC.CCR7.SELL p009
## 3 p009_T0D_ILE_LPS_3p_AAACCTGAGTAGGCCA-1 Endth/Cap.PLVAP.FLT1 p009
## 4 p009_T0D_ILE_LPS_3p_AAACCTGAGTCAAGGC-1 T/NK/ILC.CCR7.SELL p009
## 5 p009_T0D_ILE_LPS_3p_AAACCTGCAACGATGG-1 B.IGHD.FCER2 p009
## 6 p009_T0D_ILE_LPS_3p_AAACCTGCAATCCGAT-1 T/NK/ILC.CCR7.SELL p009
## time_point tissue sample_site seqDirection barcode
## 1 T0D ILE LPS 3p AAACCTGAGACTGTAA-1
## 2 T0D ILE LPS 3p AAACCTGAGTAGCCGA-1
## 3 T0D ILE LPS 3p AAACCTGAGTAGGCCA-1
## 4 T0D ILE LPS 3p AAACCTGAGTCAAGGC-1
## 5 T0D ILE LPS 3p AAACCTGCAACGATGG-1
## 6 T0D ILE LPS 3p AAACCTGCAATCCGAT-1
## tierNident tier1 tier2 tier3 tier4 tier5 tier6 tier7 tier8
## 1 T0C0.T1C0.T2C1.T3C0 T0C0 T1C0 T2C1 T3C0 <NA> <NA> <NA> <NA>
## 2 T0C0.T1C0.T2C1.T3C1 T0C0 T1C0 T2C1 T3C1 <NA> <NA> <NA> <NA>
## 3 T0C0.T1C8.T2C1 T0C0 T1C8 T2C1 <NA> <NA> <NA> <NA> <NA>
## 4 T0C0.T1C0.T2C1.T3C1 T0C0 T1C0 T2C1 T3C1 <NA> <NA> <NA> <NA>
## 5 T0C0.T1C1.T2C0.T3C1.T4C3 T0C0 T1C1 T2C0 T3C1 T4C3 <NA> <NA> <NA>
## 6 T0C0.T1C0.T2C1.T3C1 T0C0 T1C0 T2C1 T3C1 <NA> <NA> <NA> <NA>
## tier9 tier10
## 1 <NA> <NA>
## 2 <NA> <NA>
## 3 <NA> <NA>
## 4 <NA> <NA>
## 5 <NA> <NA>
## 6 <NA> <NA>End cluster diversity analysis
In the paper, we used a diversity metric, the Gini-Simpson’s Index, to measure diversity of patients in our end clusters to determine if the cluster was powered for differential analysis. Gini-Simpson’s index is calculated as
\(diversity = 1 - \lambda = \sum_{i=1}^{R} p_{i}^{2}\)
R is the total number of patients in the dataset while \(p_{i}\) is the proportional abundance of each patient in the end clusters. lambda is subtracted from 1 because in datasets with fewer patients, lambda will be inflated.
1 indicates equal representation of all patients within a subset and 0 indicates a completely patient specific subset
tierNdiversity <- srobj@meta.data %>% group_by(tierNident) %>%
summarize(diversity = 1 - sum(((table(patient)) / (length(patient)))^2))
curatedNamediversity <- srobj@meta.data %>% group_by(curatedname) %>%
summarize(diversity = 1 - sum(((table(patient)) / (length(patient)))^2))We used patient diversity among end clusters to inform manual curation
s1 <- ggplot(tierNdiversity,aes(x=diversity))+ geom_histogram() + ggtitle("raw ITC results")
s2 <- ggplot(curatedNamediversity,aes(x=diversity))+ geom_histogram() + ggtitle("after manual curation")
s1+s2
Building a tree
meta <- srobj@meta.data
meta$pathString <- meta$tierNident
meta$pathString <- gsub('\\.','/',meta$pathString)
itctree <- as.Node(meta)
print(itctree,'majortype','subtype','groundtruth','curatedname',limit=20)## levelName majortype subtype groundtruth
## 1 T0C0 NA NA NA
## 2 ¦--T1C0 NA NA NA
## 3 ¦ ¦--T2C1 NA NA NA
## 4 ¦ ¦ ¦--T3C0 NA NA NA
## 5 ¦ ¦ °--T3C1 NA NA NA
## 6 ¦ ¦--T2C0 NA NA NA
## 7 ¦ ¦ ¦--T3C0 NA NA NA
## 8 ¦ ¦ ¦ ¦--T4C0 NA NA NA
## 9 ¦ ¦ ¦ ¦ ¦--T5C8 NA NA NA
## 10 ¦ ¦ ¦ ¦ ¦--T5C7 NA NA NA
## 11 ¦ ¦ ¦ ¦ ¦--T5C0 NA NA NA
## 12 ¦ ¦ ¦ ¦ ¦--T5C5 NA NA NA
## 13 ¦ ¦ ¦ ¦ ¦--T5C6 NA NA NA
## 14 ¦ ¦ ¦ ¦ ¦--T5C1 NA NA NA
## 15 ¦ ¦ ¦ ¦ ¦--T5C9 NA NA NA
## 16 ¦ ¦ ¦ ¦ ¦--T5C3 NA NA NA
## 17 ¦ ¦ ¦ ¦ ¦ ¦--T6C0 NA NA NA
## 18 ¦ ¦ ¦ ¦ ¦ ¦--T6C1 NA NA NA
## 19 ¦ ¦ ¦ ¦ ¦ °--T6C2 NA NA NA
## 20 ¦ ¦ ¦ ¦ °--... 2 nodes w/ 0 sub NA NA NA
## 21 ¦ ¦ ¦ °--... 3 nodes w/ 5 sub NA NA NA
## 22 ¦ ¦ °--... 3 nodes w/ 23 sub NA NA NA
## 23 ¦ °--... 1 nodes w/ 26 sub NA NA NA
## 24 °--... 11 nodes w/ 403 sub NA NA NA
## curatedname
## 1
## 2
## 3
## 4 T/NK/ILC.MAF.RPS26
## 5 T/NK/ILC.CCR7.SELL
## 6
## 7
## 8
## 9 T.RORA.CCR6
## 10 T.RORA.CCR6
## 11 T.RORA.CCR6
## 12 T.RORA.CCR6
## 13 T.RORA.CCR6
## 14 T.RORA.CCR6
## 15 T.RORA.CCR6
## 16
## 17 T.RORA.CCR6
## 18 T.RORA.CCR6
## 19 T.RORA.CCR6
## 20
## 21
## 22
## 23
## 24itcphylo <- as.phylo(itctree)
#For curatednames
#itcphylo$tip.label <- itctree$Get('curatedname')[!is.na(itctree$Get('curatedname'))]
#For tierNident
itcphylo$tip.label <- itctree$Get('tierNident')[!is.na(itctree$Get('tierNident'))]
ggtree(itcphylo,branch.length='edge.length',layout='circular')
Display Gini-Simpson’s index next to the tree, in the paper’s style
plotdendrogram <- function(phyloObj,title) {
dd.row <- as.dendrogram(phyloObj)
ddata_x <- dendro_data(dd.row)
ddata_L <- label(ddata_x)
ddata_L$label <- str_replace_all(ddata_L$label, "−", "-")
#For majortype
#ddata_L$group <- gsub("\\..*$", "", ddata_L$label)
#For subtype
ddata_L$group <- gsub("^([^.]+)\\.([^.]+)\\.(.+)$","\\2",ddata_L$label)
ddata_n <- segment(ddata_x) %>% filter(yend!=0 & yend!=y)
celltype.colors <- primary.colors(length(unique(ddata_L$group)))
names(celltype.colors) <- unique(ddata_L$group)
ddata_L$count = as.numeric(table(factor(srobj$tierNident))[itcphylo$tip.label])
ddata_L$diversity = srobj@meta.data %>% group_by(tierNident) %>%
summarize(diversity = 1 - sum(((table(patient)) / (length(patient)))^2)) %>%
column_to_rownames('tierNident') %>%
.[phyloObj$tip.label, ]
y.scl = .05
plt <- ggplot(segment(ddata_x)) +
geom_segment(aes(x=x, y=y*y.scl, xend=xend, yend=yend*y.scl)) +
geom_text(data=ddata_L, angle = 0, hjust = 0, size = 1.5,
aes(label=label, x=x, y=-.4, color=group)) +
geom_text(data=ddata_L, angle = 0, hjust = 1, size = 1.5,
aes(label=count, x=x, y=-0.39),color="black") +
geom_tile(data=ddata_L, aes(x = x, y=-0.11, fill=diversity), height=0.15) +
scale_color_manual(values=celltype.colors) +
scale_fill_gradient(low = "white", high = "black", limits=c(0,1)) +
coord_flip(clip = 'off') +
scale_y_reverse(limits = c(max(segment(ddata_x)$y*y.scl),-4)) +
#breaks = c(1, 0.75, 0.5, 0.25, 0),
#labels = c(1, 0.75, 0.5, 0.25, 0)) +
scale_x_reverse() +
theme_classic() +
theme(legend.position = "bottom",
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
ylab("Split Distance") +
xlab("Clusters") +
ggtitle(title)
return(plt)
}p1 <- plotdendrogram(itcphylo, title='original hierarchy tree')
p1
Annotation
In the paper, tiers 1 and 2 were annotated manually. End clusters were named based on the tier1.tier2 combination they belonged to, followed by 2 genes either manually chosen or chosen on a heuristic rank score among its tier2 group. The rank score is calculated as follows on the results of a 1 vs. all wilcoxon rank sum test:
\[-\log (p.adj+(1*10^{-310})) * \log{foldchange} * \small\cfrac{pct.1}{pct.2+1*10^{-300}}\]
We also created a shiny app for understanding how the weighting the components of the rank score affects gene choice
We can view the original ITC tree with curated names to get an idea of how we collapsed clusters
curatedNamediversity <- srobj@meta.data %>% group_by(curatedname) %>%
summarize(diversity = 1 - sum(((table(patient)) / (length(patient)))^2)) itcphylo$tip.label <- itctree$Get('curatedname')[!is.na(itctree$Get('curatedname'))]
p2 <- plotdendrogram(as.phylo(itcphylo),"original hierarchy with curated names")
p2
It is important to note that ITC can split major cell types in the earliest stages, so that the final tree may misrepresent the relationships of the end clusters. To reconstruct those relationships, we hierarchically clustered euclidean distances between gene medians among end clusters, building a binary tree
Binary tree creation
While curating the end clusters, we wanted to better understand the distance between clusters. In the atlas figures 3 and 4, we performed hierarchical clustering of curated end clusters (not to be confused with ITC: hclus produces a binary tree). Hierarchical clustering was performed on median expression of a subset of highly DE genes (approximately 4500) using complete linkage and distance calculated with Pearson correlation. DE was performed by pairwise expression tests of end clusters.
Idents(srobj) <- srobj@meta.data$curatedname
#markers are selected by pairwise expression tests
de.df.sct <- lapply(Idents(srobj), function(x) {return(FindMarkers(srobj,ident.1=x))})
de.df.sct <- bind_rows(de.df.sct)genes.use <- de.df.sct$gene
scaled.data = Matrix::Matrix(srobj@assays$SCT@counts[de.df.sct$gene,])
scaled.data.t = t(scaled.data)
scaled.data.t = as.data.frame(scaled.data.t)
scaled.data.t$cellsubsets = srobj$curatedname
agg.clst.cntrs = aggregate(scaled.data.t[, -which(colnames(scaled.data.t) == "cellsubsets")],
list(scaled.data.t$cellsubsets), median)
colnames(agg.clst.cntrs) <- c("cellsubsets", colnames(agg.clst.cntrs)[2:length(colnames(agg.clst.cntrs))])
clst.cntrs <- agg.clst.cntrs %>%
column_to_rownames("cellsubsets") %>%
t %>% as.data.frameThe package pvclust was used for hierarchical clustering, bootstrapping sampling genes. Here I use one bootstrap due to computational limitations
library(pvclust)
result <- pvclust(clst.cntrs, method.dist="cor", method.hclust="complete", nboot=1, parallel=TRUE)## Creating a temporary cluster...done:
## socket cluster with 11 nodes on host 'localhost'## Warning in pvclust.parallel(cl = cl, data = data, method.hclust =
## method.hclust, : nboot is too small for cluster size. non-parallel version is
## executed## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
## Bootstrap (r = 0.7)... Done.
## Bootstrap (r = 0.8)... Done.
## Bootstrap (r = 0.9)... Done.
## Bootstrap (r = 1.0)... Done.
## Bootstrap (r = 1.1)... Done.
## Bootstrap (r = 1.2)... Done.
## Bootstrap (r = 1.3)... Done.
## Bootstrap (r = 1.4)... Done.p3
Nasal Polyp ITC
Here is another example of an ITC tree built on another scRNAseq dataset of nasal polyps.
## Warning: Expected 10 pieces. Missing pieces filled with `NA` in 19196 rows [1,
## 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].tiers <- LoadTiersAsDF(folder = '~/tieredoutput/endclusts')nasalp1
nasalp2
nasalp3
## [1] "Thanks for reading!"sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] phylogram_2.1.0 dendextend_1.14.0 cowplot_1.1.1 phyloseq_1.32.0
## [5] rdist_0.0.5 ape_5.5 ggtree_2.2.4 treeio_1.17.0
## [9] data.tree_1.0.0 Matrix_1.3-2 ggpubr_0.4.0 glmGamPoi_1.0.0
## [13] vegan_2.5-7 lattice_0.20-44 permute_0.9-5 pheatmap_1.0.12
## [17] ggdendro_0.1.22 pvclust_2.2-0 colorRamps_2.3 networkD3_0.4
## [21] viridis_0.6.0 viridisLite_0.4.0 SeuratObject_4.0.0 Seurat_4.0.1
## [25] data.table_1.14.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5
## [29] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.1
## [33] ggplot2_3.3.3 tidyverse_1.3.1 rmarkdown_2.7
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.1 reticulate_1.20 tidyselect_1.1.1
## [4] htmlwidgets_1.5.3 Rtsne_0.15 munsell_0.5.0
## [7] codetools_0.2-18 ica_1.0-2 future_1.21.0
## [10] miniUI_0.1.1.1 withr_2.4.2 colorspace_2.0-1
## [13] Biobase_2.50.0 highr_0.9 knitr_1.33
## [16] rstudioapi_0.13 stats4_4.0.3 ROCR_1.0-11
## [19] ggsignif_0.6.1 tensor_1.5 listenv_0.8.0
## [22] labeling_0.4.2 polyclip_1.10-0 farver_2.1.0
## [25] rhdf5_2.32.4 parallelly_1.25.0 vctrs_0.3.8
## [28] generics_0.1.0 xfun_0.22 R6_2.5.0
## [31] spatstat.utils_2.1-0 assertthat_0.2.1 promises_1.2.0.1
## [34] scales_1.1.1 gtable_0.3.0 globals_0.14.0
## [37] goftest_1.2-2 rlang_0.4.11 splines_4.0.3
## [40] rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.1-0
## [43] broom_0.7.6 BiocManager_1.30.12 yaml_2.2.1
## [46] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
## [49] backports_1.2.1 httpuv_1.6.0 tools_4.0.3
## [52] ellipsis_0.3.2 spatstat.core_2.1-2 biomformat_1.16.0
## [55] jquerylib_0.1.4 RColorBrewer_1.1-2 BiocGenerics_0.36.1
## [58] ggridges_0.5.3 Rcpp_1.0.6 plyr_1.8.6
## [61] progress_1.2.2 zlibbioc_1.36.0 prettyunits_1.1.1
## [64] ps_1.6.0 rpart_4.1-15 deldir_0.2-10
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## [70] haven_2.4.1 ggrepel_0.9.1 cluster_2.1.2
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## [76] openxlsx_4.2.3 lmtest_0.9-38 reprex_2.0.0
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